van der Kloet, F. | Search bioRxiv

bioRxiv2026

Transposon library anomaly reveals importance of cell wall teichoic acids for kin discrimination

Genome-wide transposon insertion sequencing (Tn-seq) is a powerful tool to measure the importance of genes for growth. In this study, we applied Tn-seq to the Gram-positive model system Bacillus subtilis, and found that after growth in liquid medium the transposon library lacked transposon insertions in several genes related to lipoteichoic acid biosynthesis and cell wall teichoic acid modification. This was unexpected since these genes are not essential for normal growth. By growing the transposon library as a confluent layer of cells, and as discrete colonies, we found that these genes are only important when the transposon library is grown as a confluent layer. Thus, growing the transposon library as a mixed population reduces the fitness of teichoic acid mutants. We provide further evidence showing that this anomaly is caused by the production of multiple autologous antimicrobials and/or toxins in the growth medium. These findings show that growing genome-wide mutant libraries as mixed cultures can influence the mutant library composition.

microbiology↗

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗